Automated robot-assisted surgical skill evaluation: Predictive analytics approach.

BACKGROUND: Surgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot-assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise. METHODS: Eight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise - novice and expert. Three classification methods - k-nearest neighbours, logistic regression and support vector machines - are applied. RESULTS: The result shows that the proposed framework can classify surgeons' expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task. CONCLUSION: This study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.

Distance-based time series classification approach for task recognition with application in surgical robot autonomy.

BACKGROUND: Robotic-assisted surgery allows surgeons to perform many types of complex operations with greater precision than is possible with conventional surgery. Despite these advantages, in current systems, a surgeon should communicate with the device directly and manually. To allow the robot to adjust parameters such as camera position, the system needs to know automatically what task the surgeon is performing. METHODS: A distance-based time series classification framework has been developed which measures dynamic time warping distance between temporal trajectory data of robot arms and classifies surgical tasks and gestures using a k-nearest neighbor algorithm. RESULTS: Results on real robotic surgery data show that the proposed framework outperformed state-of-the-art methods by up to 9% across three tasks and by 8% across gestures. CONCLUSION: The proposed framework is robust and accurate. Therefore, it can be used to develop adaptive control systems that will be more responsive to surgeons' needs by identifying next movements of the surgeon. Copyright © 2016 John Wiley & Sons, Ltd.